Machine learning and IoT for automatic presence detection of workers on fall protection life lines

After a general introduction to the industrial issue we dealt with, the talk will give a detailed description of the machine learning training process and the underlying architecture we built on top of Tornado, Jupyter, pandas, HDF5, Bokeh and pyRserve (for Python-R communication).

Workers on fall protection life lines usually operate in unsupervised areas (eg. roofing). In case of an accident, the lack of information about the presence and location of workers may delay the execution of the expected contingency plans. Moreover, fall protection life lines require regular maintenance to preserve safety. The lack of real-time knowledge about their actual usage and loads forces the planning of maintenance interventions to be mostly based on repetitive and non-optimal regulations.

In our solution, we combined Internet of Things technologies for collecting raw data about the life line dynamics, with Machine Learning algorithms applied to raw data analysis in order to classify different dynamic profiles into the relevant worker presence events.